{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "fd46e456",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "        pass\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33a230cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:26:56.661352Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# input folder\n",
    "from pathlib import Path\n",
    "Path('/kaggle/working/oral-diseases/').mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "920cd5e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:26:56.686910Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/kaggle/working/oral-diseases/caries')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shutil\n",
    "import os\n",
    "\n",
    "# Caries.\n",
    "# The source path\n",
    "caries_file_path = Path('/kaggle/input/oral-diseases/Data caries/Data caries/caries augmented data set/preview')\n",
    "\n",
    "# The destination path\n",
    "caries_new_directory = Path('/kaggle/working/oral-diseases/caries')\n",
    "\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(caries_file_path, caries_new_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c9bd29f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:27:12.246716Z",
     "iopub.status.busy": "2024-01-05T05:27:12.246412Z",
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     "shell.execute_reply": "2024-01-05T05:28:12.834105Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/kaggle/working/oral-diseases/hypodontia')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculus\n",
    "# The source path\n",
    "calculus_file_path = Path('/kaggle/input/oral-diseases/Calculus/Calculus')\n",
    "# The destination path\n",
    "calculus_new_directory = Path('/kaggle/working/oral-diseases/calculus')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(calculus_file_path, calculus_new_directory)\n",
    "\n",
    "#Gingivitis\n",
    "# The source path\n",
    "gingivitis_file_path = Path('/kaggle/input/oral-diseases/Gingivitis/Gingivitis')\n",
    "# The destination path\n",
    "gingivitis_new_directory = Path('/kaggle/working/oral-diseases/gingivitis')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(gingivitis_file_path, gingivitis_new_directory)\n",
    "\n",
    "#Ulcers\n",
    "# The source path\n",
    "ulcer_file_path = Path('/kaggle/input/oral-diseases/Mouth Ulcer/Mouth Ulcer/Mouth_Ulcer_augmented_DataSet/preview')\n",
    "# The destination path\n",
    "ulcer_new_directory = Path('/kaggle/working/oral-diseases/ulcers')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(ulcer_file_path, ulcer_new_directory)\n",
    "\n",
    "#Tooth Discoloration\n",
    "# The source path\n",
    "toothDiscoloration_file_path = Path('/kaggle/input/oral-diseases/Tooth Discoloration/Tooth Discoloration /Tooth_discoloration_augmented_dataser/preview')\n",
    "# The destination path\n",
    "toothDiscoloration_new_directory = Path('/kaggle/working/oral-diseases/toothDiscoloration')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(toothDiscoloration_file_path, toothDiscoloration_new_directory)\n",
    "\n",
    "#hypodontia\n",
    "# The source path\n",
    "hypodontia_file_path = Path('/kaggle/input/oral-diseases/hypodontia/hypodontia')\n",
    "# The destination path\n",
    "hypodontia_new_directory = Path('/kaggle/working/oral-diseases/hypodontia')\n",
    "# Copying data from input folder to working folder\n",
    "shutil.copytree(hypodontia_file_path, hypodontia_new_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "851f77a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:12.868330Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['caries',\n",
       " 'hypodontia',\n",
       " 'gingivitis',\n",
       " 'toothDiscoloration',\n",
       " 'calculus',\n",
       " 'ulcers']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The different classes we have from the folders\n",
    "import os\n",
    "build_dir = \"/kaggle/working/oral-diseases\"\n",
    "caries_dir = os.path.join(build_dir,'caries')\n",
    "gingivitis_dir = os.path.join(build_dir,'gingivitis')\n",
    "toothDiscoloration_dir = os.path.join(build_dir,'toothDiscoloration')\n",
    "ulcers_dir = os.path.join(build_dir,'ulcers')\n",
    "hypodontia_dir = os.path.join(build_dir,'hypodontia')\n",
    "calculus_dir = os.path.join(build_dir,'calculus')\n",
    "os.listdir(build_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93749e8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:12.899413Z",
     "iopub.status.busy": "2024-01-05T05:28:12.898705Z",
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     "shell.execute_reply": "2024-01-05T05:28:12.916243Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Showing the total number of images from each class/folder\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.DataFrame(data=[len(os.listdir(caries_dir)), len(os.listdir(gingivitis_dir)),\n",
    "                   len(os.listdir(toothDiscoloration_dir)), len(os.listdir(ulcers_dir)),\n",
    "                   len(os.listdir(hypodontia_dir)),\n",
    "                   len(os.listdir(calculus_dir))], index=['Caries', 'Gingivitis',\n",
    "                                                                    'toothDiscoloration', 'ulcers',\n",
    "                                                                    'hypodontia', 'calculus'],\n",
    "              columns=['Total Images'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d7424f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:12.940723Z",
     "iopub.status.busy": "2024-01-05T05:28:12.940411Z",
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     "shell.execute_reply": "2024-01-05T05:28:12.950971Z"
    },
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     "start_time": "2024-01-05T05:28:12.929271",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data.to_csv('data_distribution.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0b1653e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:12.975561Z",
     "iopub.status.busy": "2024-01-05T05:28:12.974709Z",
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     "shell.execute_reply": "2024-01-05T05:28:12.990570Z"
    },
    "papermill": {
     "duration": 0.02935,
     "end_time": "2024-01-05T05:28:12.993262",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Images</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Caries</th>\n",
       "      <td>2382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gingivitis</th>\n",
       "      <td>2349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toothDiscoloration</th>\n",
       "      <td>1834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ulcers</th>\n",
       "      <td>2541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypodontia</th>\n",
       "      <td>1251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calculus</th>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Total Images\n",
       "Caries                      2382\n",
       "Gingivitis                  2349\n",
       "toothDiscoloration          1834\n",
       "ulcers                      2541\n",
       "hypodontia                  1251\n",
       "calculus                    1296"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5555fe54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:13.015703Z",
     "iopub.status.busy": "2024-01-05T05:28:13.015383Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sample of caries\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42355dd6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:13.043351Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa52fb85a80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample of hypodontia\n",
    "hypodontiaimg=Image.open(\"/kaggle/working/oral-diseases/hypodontia/(25).JPG\")\n",
    "plt.imshow(hypodontiaimg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb041ec7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:13.437938Z",
     "iopub.status.busy": "2024-01-05T05:28:13.437133Z",
     "iopub.status.idle": "2024-01-05T05:28:28.387490Z",
     "shell.execute_reply": "2024-01-05T05:28:28.386504Z"
    },
    "papermill": {
     "duration": 14.965736,
     "end_time": "2024-01-05T05:28:28.390164",
     "exception": false,
     "start_time": "2024-01-05T05:28:13.424428",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (2.0.0)\r\n",
      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (0.15.1)\r\n",
      "Requirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (4.36.0)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch) (3.12.2)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch) (4.5.0)\r\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch) (1.12)\r\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch) (3.1)\r\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch) (3.1.2)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision) (1.24.3)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from torchvision) (2.31.0)\r\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision) (10.1.0)\r\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.19.4)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers) (21.3)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers) (6.0.1)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers) (2023.8.8)\r\n",
      "Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.15.0)\r\n",
      "Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.4.1)\r\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers) (4.66.1)\r\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.12.2)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers) (3.0.9)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch) (2.1.3)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (3.2.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (3.4)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (1.26.15)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->torchvision) (2023.11.17)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install torch torchvision transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "451f13c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:28.417360Z",
     "iopub.status.busy": "2024-01-05T05:28:28.416636Z",
     "iopub.status.idle": "2024-01-05T05:28:56.862661Z",
     "shell.execute_reply": "2024-01-05T05:28:56.861808Z"
    },
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     "duration": 28.462217,
     "end_time": "2024-01-05T05:28:56.865224",
     "exception": false,
     "start_time": "2024-01-05T05:28:28.403007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from transformers import ViTForImageClassification\n",
    "from transformers import ViTFeatureExtractor\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27588cbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:56.892079Z",
     "iopub.status.busy": "2024-01-05T05:28:56.891437Z",
     "iopub.status.idle": "2024-01-05T05:28:57.146348Z",
     "shell.execute_reply": "2024-01-05T05:28:57.145418Z"
    },
    "papermill": {
     "duration": 0.270296,
     "end_time": "2024-01-05T05:28:57.148418",
     "exception": false,
     "start_time": "2024-01-05T05:28:56.878122",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "841707de57eb4f55a30b242d76ca6d37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "preprocessor_config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "class KidneyDataset(Dataset):\n",
    "    def __init__(self, root_dir, feature_extractor):\n",
    "        self.root_dir = root_dir\n",
    "        self.feature_extractor = feature_extractor\n",
    "        self.classes = ['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus']\n",
    "        self.data = []\n",
    "        for idx, cls in enumerate(self.classes):\n",
    "            path = os.path.join(root_dir, cls)\n",
    "            for img in os.listdir(path):\n",
    "                self.data.append((os.path.join(path, img), idx))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        img_path, label = self.data[idx]\n",
    "        image = Image.open(img_path)  # Load image as PIL image\n",
    "        image = image.resize((224, 224))  # Resize image\n",
    "\n",
    "        # Convert the PIL image to a NumPy array\n",
    "        image = np.array(image)\n",
    "\n",
    "        # Check if the image has an alpha channel (4 channels), and if so, remove it\n",
    "        if image.shape[-1] == 4:\n",
    "            image = image[..., :3]\n",
    "\n",
    "        image = self.feature_extractor(images=image, return_tensors=\"pt\").pixel_values[0]\n",
    "        return image, label\n",
    "\n",
    "\n",
    "\n",
    "root_dir = \"/kaggle/working/oral-diseases\"\n",
    "feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')\n",
    "dataset = KidneyDataset(root_dir, feature_extractor)\n",
    "\n",
    "# Split the dataset into train and test sets\n",
    "train_size = int(0.8 * len(dataset))\n",
    "test_size = len(dataset) - train_size\n",
    "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e230d698",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:57.174939Z",
     "iopub.status.busy": "2024-01-05T05:28:57.174588Z",
     "iopub.status.idle": "2024-01-05T05:28:59.880137Z",
     "shell.execute_reply": "2024-01-05T05:28:59.879353Z"
    },
    "papermill": {
     "duration": 2.721471,
     "end_time": "2024-01-05T05:28:59.882511",
     "exception": false,
     "start_time": "2024-01-05T05:28:57.161040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6c221c3ff96434cbb041c909b6e06b7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9e97c9020304ec7a4dbb71dd9648278",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/346M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "class ViTClassifier(nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super(ViTClassifier, self).__init__()\n",
    "        self.vit = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224-in21k')\n",
    "        self.vit.classifier = nn.Linear(self.vit.classifier.in_features, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.vit(pixel_values=x).logits\n",
    "        return x\n",
    "\n",
    "model = ViTClassifier(num_classes=6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "034b4be9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:59.910957Z",
     "iopub.status.busy": "2024-01-05T05:28:59.910133Z",
     "iopub.status.idle": "2024-01-05T05:28:59.915201Z",
     "shell.execute_reply": "2024-01-05T05:28:59.914301Z"
    },
    "papermill": {
     "duration": 0.021135,
     "end_time": "2024-01-05T05:28:59.917149",
     "exception": false,
     "start_time": "2024-01-05T05:28:59.896014",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, precision_recall_fscore_support\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "afb652f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:28:59.944388Z",
     "iopub.status.busy": "2024-01-05T05:28:59.944061Z",
     "iopub.status.idle": "2024-01-05T05:29:00.312579Z",
     "shell.execute_reply": "2024-01-05T05:29:00.311590Z"
    },
    "papermill": {
     "duration": 0.384962,
     "end_time": "2024-01-05T05:29:00.315068",
     "exception": false,
     "start_time": "2024-01-05T05:28:59.930106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2743b94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T05:29:00.342633Z",
     "iopub.status.busy": "2024-01-05T05:29:00.341760Z",
     "iopub.status.idle": "2024-01-05T16:25:40.864415Z",
     "shell.execute_reply": "2024-01-05T16:25:40.863435Z"
    },
    "papermill": {
     "duration": 39400.55976,
     "end_time": "2024-01-05T16:25:40.887670",
     "exception": false,
     "start_time": "2024-01-05T05:29:00.327910",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 1.7520585211172497, Accuracy: 0.22623900450547094\n",
      "Epoch 2, Loss: 1.7298272227587765, Accuracy: 0.24415361510405492\n",
      "Epoch 3, Loss: 1.4953991186945406, Accuracy: 0.3767431881570478\n",
      "Epoch 4, Loss: 1.295923579433193, Accuracy: 0.46492169062432953\n",
      "Epoch 5, Loss: 1.1770536403541696, Accuracy: 0.5174855181291569\n",
      "Epoch 6, Loss: 1.0755103263136458, Accuracy: 0.560394765071873\n",
      "Epoch 7, Loss: 0.9872992434730269, Accuracy: 0.5919330615747693\n",
      "Epoch 8, Loss: 0.9274166981651358, Accuracy: 0.6218622613173139\n",
      "Epoch 9, Loss: 0.8904509159595999, Accuracy: 0.6397768719158978\n",
      "Epoch 10, Loss: 0.8341968558626632, Accuracy: 0.659193306157477\n",
      "Epoch 11, Loss: 0.8012042241553737, Accuracy: 0.6750697275262819\n",
      "Epoch 12, Loss: 0.7606957146566208, Accuracy: 0.6921261531860116\n",
      "Epoch 13, Loss: 0.7462707124140164, Accuracy: 0.6993134520489166\n",
      "Epoch 14, Loss: 0.7243632755867423, Accuracy: 0.7063934777944647\n",
      "Epoch 15, Loss: 0.6855748084514108, Accuracy: 0.7253808195666166\n",
      "Epoch 16, Loss: 0.6551780956861091, Accuracy: 0.7352499463634413\n",
      "Epoch 17, Loss: 0.6525212006822024, Accuracy: 0.7380390474147178\n",
      "Epoch 18, Loss: 0.6339759614369641, Accuracy: 0.7465136236859043\n",
      "Epoch 19, Loss: 0.6004254623022798, Accuracy: 0.7644282342844884\n",
      "Epoch 20, Loss: 0.6025590796584952, Accuracy: 0.7601373095902166\n",
      "Epoch 21, Loss: 0.5665521810520185, Accuracy: 0.7692555245655439\n",
      "Epoch 22, Loss: 0.53418877690214, Accuracy: 0.7914610598583995\n",
      "Epoch 23, Loss: 0.5299800759411022, Accuracy: 0.7884574125724093\n",
      "Epoch 24, Loss: 0.5082335580293447, Accuracy: 0.7985410856039477\n",
      "Epoch 25, Loss: 0.481522636003282, Accuracy: 0.8061574769362798\n",
      "Epoch 26, Loss: 0.48562035076830484, Accuracy: 0.8036901952370736\n",
      "Epoch 27, Loss: 0.493306167270631, Accuracy: 0.8063720231709933\n",
      "Epoch 28, Loss: 0.4667998972820909, Accuracy: 0.8151684187942502\n",
      "Epoch 29, Loss: 0.47222745020503865, Accuracy: 0.8106629478652649\n",
      "Epoch 30, Loss: 0.4524336451639051, Accuracy: 0.8211757133662304\n",
      "Epoch 31, Loss: 0.4159463064311302, Accuracy: 0.8317957519845527\n",
      "Epoch 32, Loss: 0.41676693353546806, Accuracy: 0.8345848530358292\n",
      "Epoch 33, Loss: 0.3788323105657346, Accuracy: 0.85260673675177\n",
      "Epoch 34, Loss: 0.3921163448017754, Accuracy: 0.8405921476078095\n",
      "Epoch 35, Loss: 0.3941941719553242, Accuracy: 0.847457627118644\n",
      "Epoch 36, Loss: 0.37699729227141976, Accuracy: 0.8519630980476293\n",
      "Epoch 37, Loss: 0.44126161693097793, Accuracy: 0.8225702638918687\n",
      "Epoch 38, Loss: 0.35049700696174413, Accuracy: 0.8603304012014589\n",
      "Epoch 39, Loss: 0.35359334973745965, Accuracy: 0.8576485732675392\n",
      "Epoch 40, Loss: 0.33057457895005404, Accuracy: 0.8637631409568762\n",
      "Epoch 41, Loss: 0.315645214782594, Accuracy: 0.8707358935850675\n",
      "Epoch 42, Loss: 0.28912360767183237, Accuracy: 0.8816777515554602\n",
      "Epoch 43, Loss: 0.2853873301399489, Accuracy: 0.8850032181935207\n",
      "Epoch 44, Loss: 0.3864751455871618, Accuracy: 0.8438103411285132\n",
      "Epoch 45, Loss: 0.3495036950207328, Accuracy: 0.8600085818493886\n",
      "Epoch 46, Loss: 0.32288267338418797, Accuracy: 0.8693413430594293\n",
      "Epoch 47, Loss: 0.3346910195246543, Accuracy: 0.8660158764213688\n",
      "Epoch 48, Loss: 0.2787952238234551, Accuracy: 0.8880068654795108\n",
      "Epoch 49, Loss: 0.22104044504183, Accuracy: 0.9107487663591504\n",
      "Epoch 50, Loss: 0.2672761548565675, Accuracy: 0.8916541514696417\n",
      "Epoch 51, Loss: 0.2475338946670702, Accuracy: 0.8935850675820639\n",
      "Epoch 52, Loss: 0.241330793254996, Accuracy: 0.8968032611027676\n",
      "Epoch 53, Loss: 0.27727376853358254, Accuracy: 0.8853250375455911\n",
      "Epoch 54, Loss: 0.27879794495665045, Accuracy: 0.8856468568976614\n",
      "Epoch 55, Loss: 0.223407802350615, Accuracy: 0.9071014803690195\n",
      "Epoch 56, Loss: 0.2206833448949946, Accuracy: 0.9061360223128084\n",
      "Epoch 57, Loss: 0.24592584198060102, Accuracy: 0.8984123578631195\n",
      "Epoch 58, Loss: 0.22157711608411923, Accuracy: 0.9074232997210899\n",
      "Epoch 59, Loss: 0.22438940027575582, Accuracy: 0.9079596653078739\n",
      "Epoch 60, Loss: 0.2578428570844539, Accuracy: 0.8927268826432097\n",
      "Epoch 61, Loss: 0.25050456967357904, Accuracy: 0.8975541729242652\n",
      "Epoch 62, Loss: 0.2111655215107619, Accuracy: 0.9097833083029393\n",
      "Epoch 63, Loss: 0.1818331840818059, Accuracy: 0.9207251662733319\n",
      "Epoch 64, Loss: 0.1851003420003371, Accuracy: 0.9185797039261961\n",
      "Epoch 65, Loss: 0.22592158382120606, Accuracy: 0.9036687406136023\n",
      "Epoch 66, Loss: 0.20759268438647668, Accuracy: 0.9116069512980047\n",
      "Epoch 67, Loss: 0.1942577896040086, Accuracy: 0.9186869770435528\n",
      "Epoch 68, Loss: 0.21446960677762758, Accuracy: 0.9102124007723664\n",
      "Epoch 69, Loss: 0.17712693392260842, Accuracy: 0.9221197167989702\n",
      "Epoch 70, Loss: 0.21544307241956256, Accuracy: 0.9084960308946578\n",
      "Epoch 71, Loss: 0.19249697013643302, Accuracy: 0.9197597082171208\n",
      "Epoch 72, Loss: 0.17359961310325012, Accuracy: 0.9231924479725381\n",
      "Epoch 73, Loss: 0.18653794182847216, Accuracy: 0.9197597082171208\n",
      "Epoch 74, Loss: 0.18767573369973123, Accuracy: 0.9220124436816134\n",
      "Epoch 75, Loss: 0.212552757270328, Accuracy: 0.9110705857112208\n",
      "Epoch 76, Loss: 0.1646333133470114, Accuracy: 0.928877923192448\n",
      "Epoch 77, Loss: 0.18523555979362294, Accuracy: 0.9191160695129801\n",
      "Epoch 78, Loss: 0.28475551110730596, Accuracy: 0.8829650289637417\n",
      "Epoch 79, Loss: 0.16984188733367275, Accuracy: 0.9257670027891011\n",
      "Epoch 80, Loss: 0.177198713645339, Accuracy: 0.9224415361510405\n",
      "Epoch 81, Loss: 0.15077410123872328, Accuracy: 0.9339197597082172\n",
      "Epoch 82, Loss: 0.19206250223095134, Accuracy: 0.9215833512121863\n",
      "Epoch 83, Loss: 0.17785613349804733, Accuracy: 0.9241579060287491\n",
      "Epoch 84, Loss: 0.25188872127870277, Accuracy: 0.8947650718729886\n",
      "Epoch 85, Loss: 0.18497149640860114, Accuracy: 0.9221197167989702\n",
      "Epoch 86, Loss: 0.15510022407397628, Accuracy: 0.9320961167131517\n",
      "Epoch 87, Loss: 0.1596467785779642, Accuracy: 0.9280197382535936\n",
      "Epoch 88, Loss: 0.1612600453440355, Accuracy: 0.9295215618965887\n",
      "Epoch 89, Loss: 0.17799360087549645, Accuracy: 0.9258742759064579\n",
      "Epoch 90, Loss: 0.15844585692943775, Accuracy: 0.9285561038403776\n",
      "Epoch 91, Loss: 0.15013037472708177, Accuracy: 0.9348852177644282\n",
      "Epoch 92, Loss: 0.2445453374506268, Accuracy: 0.9020596438532503\n",
      "Epoch 93, Loss: 0.17931180114360296, Accuracy: 0.9230851748551813\n",
      "Epoch 94, Loss: 0.2109168502055619, Accuracy: 0.9120360437674319\n",
      "Epoch 95, Loss: 0.14784715229275394, Accuracy: 0.9337052134735035\n",
      "Epoch 96, Loss: 0.1527873733757089, Accuracy: 0.9330615747693628\n",
      "Epoch 97, Loss: 0.2211368764457229, Accuracy: 0.908388757777301\n",
      "Epoch 98, Loss: 0.15699262941919573, Accuracy: 0.9287706500750912\n",
      "Epoch 99, Loss: 0.5081777350645359, Accuracy: 0.8087320317528427\n",
      "Epoch 100, Loss: 0.2145645530856125, Accuracy: 0.9126796824715726\n"
     ]
    }
   ],
   "source": [
    "train_losses = []\n",
    "train_accuracies = []\n",
    "num_epochs = 100\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for inputs, labels in train_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "    epoch_loss = running_loss / len(train_loader)\n",
    "    epoch_acc = correct / total\n",
    "    train_losses.append(epoch_loss)\n",
    "    train_accuracies.append(epoch_acc)\n",
    "\n",
    "    print(f'Epoch {epoch + 1}, Loss: {epoch_loss}, Accuracy: {epoch_acc}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9bf8a0b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:25:40.936115Z",
     "iopub.status.busy": "2024-01-05T16:25:40.935635Z",
     "iopub.status.idle": "2024-01-05T16:26:31.657118Z",
     "shell.execute_reply": "2024-01-05T16:26:31.655931Z"
    },
    "papermill": {
     "duration": 50.770662,
     "end_time": "2024-01-05T16:26:31.681181",
     "exception": false,
     "start_time": "2024-01-05T16:25:40.910519",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7563277563277563\n",
      "Precision: 0.7571651224880669\n",
      "Recall: 0.7455785090289941\n",
      "F1 Score: 0.7493921078857695\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "            caries       0.79      0.70      0.74       454\n",
      "        gingivitis       0.67      0.77      0.72       469\n",
      "toothDiscoloration       0.80      0.69      0.74       365\n",
      "            ulcers       0.87      0.90      0.89       514\n",
      "        hypodontia       0.86      0.79      0.83       238\n",
      "          calculus       0.56      0.62      0.58       291\n",
      "\n",
      "          accuracy                           0.76      2331\n",
      "         macro avg       0.76      0.75      0.75      2331\n",
      "      weighted avg       0.76      0.76      0.76      2331\n",
      "\n",
      "[[320  34  41  31  14  14]\n",
      " [  7 359   8   1   5  89]\n",
      " [ 34  23 253  36   3  16]\n",
      " [ 29   1  14 463   7   0]\n",
      " [ 11  14   0   0 189  24]\n",
      " [  5 103   2   0   2 179]]\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        all_preds.extend(preds.cpu().numpy())\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='macro')\n",
    "print(f\"Accuracy: {accuracy}\\nPrecision: {precision}\\nRecall: {recall}\\nF1 Score: {f1}\")\n",
    "print(classification_report(all_labels, all_preds, target_names=['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus']))\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "print(conf_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "788c02e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:31.723344Z",
     "iopub.status.busy": "2024-01-05T16:26:31.722986Z",
     "iopub.status.idle": "2024-01-05T16:26:32.243918Z",
     "shell.execute_reply": "2024-01-05T16:26:32.243005Z"
    },
    "papermill": {
     "duration": 0.545043,
     "end_time": "2024-01-05T16:26:32.246510",
     "exception": false,
     "start_time": "2024-01-05T16:26:31.701467",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_losses, label='Training Loss')\n",
    "plt.title('Training Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_accuracies, label='Training Accuracy')\n",
    "plt.title('Training Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ccd74fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:32.291280Z",
     "iopub.status.busy": "2024-01-05T16:26:32.290551Z",
     "iopub.status.idle": "2024-01-05T16:26:32.298332Z",
     "shell.execute_reply": "2024-01-05T16:26:32.297551Z"
    },
    "papermill": {
     "duration": 0.032086,
     "end_time": "2024-01-05T16:26:32.300250",
     "exception": false,
     "start_time": "2024-01-05T16:26:32.268164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'Model': ['ViTClassifier'],\n",
    "    'Accuracy': [accuracy],\n",
    "    'Precision': [precision],\n",
    "    'Recall': [recall],\n",
    "    'F1 Score': [f1]\n",
    "})\n",
    "df.to_csv('model_performance.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e9a04249",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:32.344589Z",
     "iopub.status.busy": "2024-01-05T16:26:32.343853Z",
     "iopub.status.idle": "2024-01-05T16:26:32.348962Z",
     "shell.execute_reply": "2024-01-05T16:26:32.348137Z"
    },
    "papermill": {
     "duration": 0.029477,
     "end_time": "2024-01-05T16:26:32.350958",
     "exception": false,
     "start_time": "2024-01-05T16:26:32.321481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"confusion_matrix.csv\", conf_matrix, delimiter=\",\", fmt='%d')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f416fce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:32.395536Z",
     "iopub.status.busy": "2024-01-05T16:26:32.394891Z",
     "iopub.status.idle": "2024-01-05T16:26:32.858448Z",
     "shell.execute_reply": "2024-01-05T16:26:32.857652Z"
    },
    "papermill": {
     "duration": 0.488397,
     "end_time": "2024-01-05T16:26:32.860744",
     "exception": false,
     "start_time": "2024-01-05T16:26:32.372347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'vit_kidney_disease_classifier.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b8e7cfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:32.905083Z",
     "iopub.status.busy": "2024-01-05T16:26:32.904743Z",
     "iopub.status.idle": "2024-01-05T16:26:33.785118Z",
     "shell.execute_reply": "2024-01-05T16:26:33.784019Z"
    },
    "papermill": {
     "duration": 0.90521,
     "end_time": "2024-01-05T16:26:33.787567",
     "exception": false,
     "start_time": "2024-01-05T16:26:32.882357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Assuming `conf_matrix` is your confusion matrix obtained from the evaluation step\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus'], yticklabels=['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.savefig('confusion_matrix.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3abe71b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:33.840063Z",
     "iopub.status.busy": "2024-01-05T16:26:33.839620Z",
     "iopub.status.idle": "2024-01-05T16:26:34.054091Z",
     "shell.execute_reply": "2024-01-05T16:26:34.053053Z"
    },
    "papermill": {
     "duration": 0.243665,
     "end_time": "2024-01-05T16:26:34.056554",
     "exception": false,
     "start_time": "2024-01-05T16:26:33.812889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ViTClassifier(\n",
       "  (vit): ViTForImageClassification(\n",
       "    (vit): ViTModel(\n",
       "      (embeddings): ViTEmbeddings(\n",
       "        (patch_embeddings): ViTPatchEmbeddings(\n",
       "          (projection): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
       "        )\n",
       "        (dropout): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (encoder): ViTEncoder(\n",
       "        (layer): ModuleList(\n",
       "          (0-11): 12 x ViTLayer(\n",
       "            (attention): ViTAttention(\n",
       "              (attention): ViTSelfAttention(\n",
       "                (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "              (output): ViTSelfOutput(\n",
       "                (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (dropout): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): ViTIntermediate(\n",
       "              (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): ViTOutput(\n",
       "              (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (layernorm_before): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (layernorm_after): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (layernorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    )\n",
       "    (classifier): Linear(in_features=768, out_features=6, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_path = '/kaggle/working/vit_kidney_disease_classifier.pth'\n",
    "model.load_state_dict(torch.load(model_path))\n",
    "model.eval()  # Set the model to evaluation mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1eaf6b0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-05T16:26:34.108824Z",
     "iopub.status.busy": "2024-01-05T16:26:34.107930Z",
     "iopub.status.idle": "2024-01-05T16:27:25.022132Z",
     "shell.execute_reply": "2024-01-05T16:27:25.021076Z"
    },
    "papermill": {
     "duration": 50.965394,
     "end_time": "2024-01-05T16:27:25.047240",
     "exception": false,
     "start_time": "2024-01-05T16:26:34.081846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7563277563277563\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "            caries       0.79      0.70      0.74       454\n",
      "        gingivitis       0.67      0.77      0.72       469\n",
      "toothDiscoloration       0.80      0.69      0.74       365\n",
      "            ulcers       0.87      0.90      0.89       514\n",
      "        hypodontia       0.86      0.79      0.83       238\n",
      "          calculus       0.56      0.62      0.58       291\n",
      "\n",
      "          accuracy                           0.76      2331\n",
      "         macro avg       0.76      0.75      0.75      2331\n",
      "      weighted avg       0.76      0.76      0.76      2331\n",
      "\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "with torch.no_grad():\n",
    "    for inputs, labels in test_loader:\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        all_preds.extend(preds.cpu().numpy())\n",
    "        all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "# Calculate accuracy and other metrics if needed\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "\n",
    "# Print the classification report\n",
    "print(classification_report(all_labels, all_preds, target_names=['caries', 'gingivitis', 'toothDiscoloration', 'ulcers', 'hypodontia', 'calculus']))\n"
   ]
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